By E. C. Parnwell
Released in 1988, this dictionary remains to be a favourite of many lecturers.
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Additional info for The New Oxford Picture Dictionary (Monolingual English Edition)
1 Structured Output Prediction . . . . . . . . . . . . . . . . . . 2 Graph-Based Semi-Supervised CRFs . . . . . . . . . . . . . . . 3 Structured Prediction with Hidden Structure . . . . . . . . . . 4 Optimization for PoMEN . . . . . . . . . . . . . . . . . . . . . Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Asymptotic Analysis for Semi-Supervised Learning . . . . . . .
This Bayesian flavor helps mitigate problems caused by the first assumption in this section: that prediction under the model θ is a good estimate of the true label distribution. Finally, let us consider how density-weighted methods make a similar approximation, but mitigate the overly strong assumption that x is sufficiently representative of the input distribution. By rewriting the equation for information gain above, we get: U Hθ (Y |x(u) ) − φ (x) ≈ Pθ (y|x)Hθ+ x,y (Y |x(u) ) . y u=1 The expression inside the summand represents the expected reduction in entropy for each instance in the pool if x is the selected query.
In other words, if two data points are in the same cluster, then they are highly likely to have the same class label. An equivalent statement can be made in terms of the lowdensity separation principle which states that the decision boundary should pass through a region of low density and therefore avoid cutting a data cluster. • Manifold Assumption: The marginal PX is supported on a lowdimensional manifold embedded in a high-dimensional ambient space. The conditional distribution P (y|x) is smooth, as a function of x, with respect to this low-dimensional manifold structure.